Hassan Saneifar, Stéphane Bonniol, Anne Laurent, Pascal Poncelet, Mathieu Roche


The Information extracted from log files of computing systems can be considered one of the important resources of information systems. In the case of Integrated Circuit design, log files generated by design tools are not exhaustively exploited. The logs of this domain are multi-source, multi-format, and have a heterogeneous and evolving structure. Moreover, they usually do not respect the grammar and the structures of natural language though they are written in English. According to features of such textual data, applying the classical methods of information extraction is not an easy task, more particularly for terminology extraction. We have previously introduced EXTERLOG approach to extract the terminology from such log files. In this paper, we introduce a new developed version of EXTERLOG guided by Web. We score the extracted terms by a Web and context based measure. We favor the more relevant terms of domain and emphasize the precision by filtering terms based on their scores. The experiments show that EXTERLOG is well-adapted terminology extraction approach from log files.


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Paper Citation

in Harvard Style

Saneifar H., Bonniol S., Laurent A., Poncelet P. and Roche M. (2009). MINING FOR RELEVANT TERMS FROM LOG FILES . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 77-84. DOI: 10.5220/0002307200770084

in Bibtex Style

author={Hassan Saneifar and Stéphane Bonniol and Anne Laurent and Pascal Poncelet and Mathieu Roche},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
SN - 978-989-674-011-5
AU - Saneifar H.
AU - Bonniol S.
AU - Laurent A.
AU - Poncelet P.
AU - Roche M.
PY - 2009
SP - 77
EP - 84
DO - 10.5220/0002307200770084